Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical ...Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.展开更多
药物不良事件信息往往以非结构化自由文本的形式分散于多元数据源中,其语义表达具有高维稀疏性,难以捕捉其跨模态语义关联,导致关键信息提取的精确率失衡,进而影响不良事件监测预警的准确性。研究提出基于知识图谱与自然语言处理(Natura...药物不良事件信息往往以非结构化自由文本的形式分散于多元数据源中,其语义表达具有高维稀疏性,难以捕捉其跨模态语义关联,导致关键信息提取的精确率失衡,进而影响不良事件监测预警的准确性。研究提出基于知识图谱与自然语言处理(Natural Language Processing,NLP)的药物不良事件监测预警方法。方法采用MEARank模型,整合NLP工具、预训练语言模型及定制化算法,通过关联性评分与位置正则化算法实现病程记录中药物、症状等关键词的精准提取。提取的关键词与结构化医学知识库中的不良事件报告相结合,构建药物不良事件知识图谱,并利用TransE模型进行知识图谱嵌入,获得实体和关系的低维向量表示。基于医疗专家经验构建的贝叶斯网络模型将知识图谱中的实体与关系转化为联合概率求解问题,实现药物不良事件风险等级的动态评估与预警。实验表明:上述方法构建的知识图谱平均倒数排名(MRR)达到0.9,TransE模型仅需40次迭代即可收敛,能够实现风险等级的精细化评估与准确预警,为临床用药安全提供可靠的技术支持。展开更多
[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同...[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同分位数下(=0.10,0.25,0.50,0.75)分别建立线性分位数混合模型拟合E2水平的动态变化,并通过赤池信息量准则(akaike information criterion,AIC)与贝叶斯信息准则(Bayesian information criteria,BIC)从中选择最优模型作为联合模型的纵向子模型。其次,基于扩展的Cox比例风险模型建立生存子模型;进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型,并通过马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法估计其关联系数()。[结果]最优子模型筛选结果显示,=0.50时,纵向子模型的AIC与BIC值最小。在=0.50下构建贝叶斯分位数联合模型。联合模型结果显示,E2水平的动态变化与乳腺癌患者的生存结局显著性相关(=0.59,HR=1.80,95%CI:1.47~2.24)。新辅助治疗是乳腺癌患者的保护因素(HR=0.155,95%CI:0.047~0.384),能够降低乳腺癌患者84.5%死亡风险。[结论]乳腺癌患者E2水平增加与不良生存预后相关,新辅助治疗可降低乳腺癌患者的死亡风险,并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散,从而提高患者的生存率。展开更多
文摘Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.
文摘药物不良事件信息往往以非结构化自由文本的形式分散于多元数据源中,其语义表达具有高维稀疏性,难以捕捉其跨模态语义关联,导致关键信息提取的精确率失衡,进而影响不良事件监测预警的准确性。研究提出基于知识图谱与自然语言处理(Natural Language Processing,NLP)的药物不良事件监测预警方法。方法采用MEARank模型,整合NLP工具、预训练语言模型及定制化算法,通过关联性评分与位置正则化算法实现病程记录中药物、症状等关键词的精准提取。提取的关键词与结构化医学知识库中的不良事件报告相结合,构建药物不良事件知识图谱,并利用TransE模型进行知识图谱嵌入,获得实体和关系的低维向量表示。基于医疗专家经验构建的贝叶斯网络模型将知识图谱中的实体与关系转化为联合概率求解问题,实现药物不良事件风险等级的动态评估与预警。实验表明:上述方法构建的知识图谱平均倒数排名(MRR)达到0.9,TransE模型仅需40次迭代即可收敛,能够实现风险等级的精细化评估与准确预警,为临床用药安全提供可靠的技术支持。
文摘[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同分位数下(=0.10,0.25,0.50,0.75)分别建立线性分位数混合模型拟合E2水平的动态变化,并通过赤池信息量准则(akaike information criterion,AIC)与贝叶斯信息准则(Bayesian information criteria,BIC)从中选择最优模型作为联合模型的纵向子模型。其次,基于扩展的Cox比例风险模型建立生存子模型;进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型,并通过马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法估计其关联系数()。[结果]最优子模型筛选结果显示,=0.50时,纵向子模型的AIC与BIC值最小。在=0.50下构建贝叶斯分位数联合模型。联合模型结果显示,E2水平的动态变化与乳腺癌患者的生存结局显著性相关(=0.59,HR=1.80,95%CI:1.47~2.24)。新辅助治疗是乳腺癌患者的保护因素(HR=0.155,95%CI:0.047~0.384),能够降低乳腺癌患者84.5%死亡风险。[结论]乳腺癌患者E2水平增加与不良生存预后相关,新辅助治疗可降低乳腺癌患者的死亡风险,并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散,从而提高患者的生存率。
